Paper Info
Title
Improved Mean Shift Algorithm with Heterogeneous Node Weights
Abstract
The conventional mean shift algorithm has been known to be sensitive to selecting a bandwidth. We present a robust mean shift algorithm with heterogeneous node weights that come from a geometric structure of a given data set. Before running MS procedure, we reconstruct un-normalized weights (a rough surface of data points) from the Delaunay Triangulation. The un-normalized weights help MS to avoid the problem of failing of misled mean shift vectors. As a result, we can obtain a more robust clustering result compared to the conventional mean shift algorithm. We also propose an alternative way to assign weights for large size datasets and noisy datasets.
Year
DOI
Venue
2010
10.1109/ICPR.2010.1026
ICPR
Keywords
Field
DocType
heterogeneous node weights,noisy datasets,mean shift vector,conventional mean shift algorithm,improved mean shift algorithm,ms procedure,data point,robust clustering result,large size datasets,delaunay triangulation,robust mean shift algorithm,un-normalized weight,image segmentation,mean shift algorithm,robustness,mean shift,bandwidth,clustering algorithms,kernel,mesh generation,clustering,noise measurement
Data point,Kernel (linear algebra),Noise measurement,Pattern recognition,Computer science,Image segmentation,Robustness (computer science),Artificial intelligence,Mean-shift,Cluster analysis,Delaunay triangulation
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Ji Won Yoon111223.94
Simon P. Wilson2489.67